Subthreshold Periodic Signal Detection by Bounded Noise‐Induced Resonance in the FitzHugh–Nagumo Neuron

Yao, Yuangen; Yang, Lijian; Wang, Can-Jun; Liu, Quan; Gui, Rong; Xiong, Juan; Yi, Ming

doi:10.1155/2018/5632650

Cited by 32 publications

(12 citation statements)

References 54 publications

(75 reference statements)

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“…According to the literature, a growing number of studies indicated that the noise generated by randomly channel opening limits neuronal response reliability 32 , influences the energy efficiency of channels 33 , and constrained the physical dimensions of axon diameters 34 . Conversely, noise can have a beneficial role by enhancing the detection of weak periodic signals via pseudo-stochastic resonance mechanism 35 or via enhancing subthreshold oscillations 36 . Consequently, it seemed that the magnitude of the channel noise is under evolutionary pressure in the biological system seeking an energy-efficiency coding system 37 .…”

Section: Pseudo Resonance Between Brain and Neuron Cordsmentioning

confidence: 99%

Mechanism behind the neuronal ephaptic coupling during synchronizing by specific brain-triggered wave as neuronal motor toolkit

Karami

Doroodmand

Taherianfar

et al. 2021

Sci Rep

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Probable mechanism behind the neuronal ephaptic coupling is investigated based on the introduction of “Brain”-triggered potential excitation signal smartly with a specific very low frequency (VLF) waves as a neuronal motor toolkit. Detection of this electric motor toolkit is attributed to in-vitro precise analyses of a neural network of snail, along to the disconnected snail’s neuronal network as a control. This is achieved via rapid (real-time) electrical signals acquisition by blind patch-clamp method during micro-electrode implanting in the neurons at the gigaseal conditions by the surgery operations. This process is based on its waveform (potential excitation signal) detection by mathematical curve fitting process. The characterized waveform of this electrical signal is “Saw Tooth” that is smartly stimulated, alternatively, by the brain during triggering the action potential’s (AP’s) hyperpolarization zone at a certain time interval at the several µs levels. Triggering the neuron cells results in (1) observing a positive shift (10.0%, depending on the intensity of the triggering wave), and (2) major promotion in the electrical current from sub nano (n) to micro (µ) amper (nA, µA) levels. Direct tracing the time domain (i.e., electrical signal vs. time) and estimation of the frequency domain (diagram of electrical response vs. the applied electrical frequencies) by the “Discrete Fast Fourier Transform” algorithm approve the presence of bilateral and reversible electrical currents between axon and dendrite. This mechanism therefore opens a novel view about the neuronal motor toolkit mechanism, versus the general knowledge about the unilateral electrical current flow from axon to dendrite operations in as neural network. The reliability of this mechanism is evaluated via (1) sequential modulation and demodulation of the snail’s neuron network by a simulation electrical functions and sequentially evaluation of the neuronal current sensitivity between pA and nA (during the promotion of the signal-to-noise ratio, via averaging of 30 ± 1 (n = 15) and recycling the electrical cycles before any neuronal response); and (2) operation of the process on the differentiated stem cells. The interstice behavior is attributed to the effective role of Ca2+ channels (besides Na+ and K+ ionic pumping), during hyper/hypo calcium processes, evidenced by inductively coupled plasma as the selected analytical method. This phenomenon is also modeled during proposing quadrupole well potential levels in the neuron systems. This mechanism therefore points to the microprocessor behavior of neuron networks. Stimulation of the neuronal system based on this mechanism, not only controls the sensitivity of neuron electrical stimulation, but also would open a light window for more efficient operating the neuronal connectivity during providing interruptions by phenomena such as neurolysis as well as an efficient treatment of neuron-based disorders.

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Section: Pseudo Resonance Between Brain and Neuron Cordsmentioning

confidence: 99%

Mechanism behind the neuronal ephaptic coupling during synchronizing by specific brain-triggered wave as neuronal motor toolkit

Karami

Doroodmand

Taherianfar

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Since the Italian researcher Benzi [15,16] proposed the stochastic resonance (SR) concept in 1981, SR has attracted the attention of many scholars and has been widely used in the fields of image enhancement [17,18], mechanical equipment fault diagnosis [19][20][21], wireless communication [22][23][24], etc.. There are some restrictions on using the method of SR [25], and scholars have studied many ways to overcome it. Reference [26] proposed a variable-scale SR method to overcome the limitation that SR can only be used under the condition that the signal frequency is smaller than 1 Hz and increases the scope of applicable conditions for SR.…”

Section: Introductionmentioning

confidence: 99%

The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

Zhang

Duan

Xue³

et al. 2020

Energies

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As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.

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“…e generation and regulation mechanism of SR in the nervous system, which depends on the frequency difference, is revealed by Guo et al [30]. e SR effect in FHN neurons was detected by Yao et al [31] and stochastic multiple resonance was found in [32] in the study of coupled excitation of FHN neurons, which can effectively detect subthreshold signals. A command filter adaptive neural network control method for multi-input multioutput nonlinear systems was proposed by Yu et al [33].…”

Section: Introductionmentioning

confidence: 99%

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

Zhang

et al. 2020

Complexity

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The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

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Subthreshold Periodic Signal Detection by Bounded Noise‐Induced Resonance in the FitzHugh–Nagumo Neuron

Cited by 32 publications

References 54 publications

Mechanism behind the neuronal ephaptic coupling during synchronizing by specific brain-triggered wave as neuronal motor toolkit

Mechanism behind the neuronal ephaptic coupling during synchronizing by specific brain-triggered wave as neuronal motor toolkit

The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

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